OpenRaiser/Intern-Atlas huggingface dataset:
On the Role of Hash-based Signatures in Quantum-Safe Internet of Things: Current Solutions and Future Directions
Venue: arXiv cs.CR 2020
Year: 2020

The Internet of Things (IoT) is gaining ground as a pervasive presence around us by enabling miniaturized things with computation and communication capabilities to collect, process, analyze, and interpret information. Consequently, trustworthy data act as fuel for applications that rely on the data generated by these things, for critical decision-making processes, data debugging, risk assessment, forensic analysis, and performance tuning. Currently, secure and reliable data communication in IoT is based on public-key cryptosystems such as Elliptic Curve Cryptosystem (ECC). Nevertheless, reliance on the security of de-facto cryptographic primitives is at risk of being broken by the impending quantum computers. Therefore, the transition from classical primitives to quantum-safe primitives is indispensable to ensure the overall security of data en route. In this paper, we investigate applications of one of the post-quantum signatures called Hash-Based Signature (HBS) schemes for the security of IoT devices in the quantum era. We give a succinct overview of the evolution of HBS schemes with emphasis on their construction parameters and associated strengths and weaknesses. Then, we outline the striking features of HBS schemes and their significance for the IoT security in the quantum era. We investigate the optimal selection of HBS in the IoT networks with respect to their performance-constrained requirements, resource-constrained nature, and design optimization objectives. In addition to ongoing standardization efforts, we also highlight current and future research and deployment challenges along with possible solutions. Finally, we outline the essential measures and recommendations that must be adopted by the IoT ecosystem while preparing for the quantum world.
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
Venue: arXiv cs.RO 2020
Year: 2020

Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide information about the agent's confidence in the recommended actions. This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving. A method for classifying whether or not an action should be considered safe is also introduced. The performance of the ensemble RPF method is evaluated by training an agent on a highway driving scenario. It is shown that the trained agent can estimate the uncertainty of its decisions and indicate an unacceptable level when the agent faces a situation that is far from the training distribution. Furthermore, within the training distribution, the ensemble RPF agent outperforms a standard Deep Q-Network agent. In this study, the estimated uncertainty is used to choose safe actions in unknown situations. However, the uncertainty information could also be used to identify situations that should be added to the training process.
A Cloud-based SDN/NFV Testbed for End-to-End Network Slicing in 4G/5G
Venue: arXiv cs.NI 2020
Year: 2020

Network slicing aims to shape 5G as a flexible, scalable, and demand-oriented network. Research communities deploy small-scale and cost-efficient testbeds in order to evaluate network slicing functionalities. We introduce a novel testbed, called 5GIIK, that provides implementation, management, and orchestration of network slices across all network domains and different access technologies. Our methodology identifies design criteria that are a superset of the features present in other state-of-the-art testbeds and determines appropriate open-source tools for implementing them. 5GIIK is one of the most comprehensive testbeds because it provides additional features and capabilities such as slice provision dynamicity, real-time monitoring of VMs, and VNF-onboarding to different VIMs. We illustrate the potentials of the proposed testbed and present initial results.
Proactive Aging Mitigation in CGRAs through Utilization-Aware Allocation
Venue: arXiv cs.AR 2020
Year: 2020

Resource balancing has been effectively used to mitigate the long-term aging effects of Negative Bias Temperature Instability (NBTI) in multi-core and Graphics Processing Unit (GPU) architectures. In this work, we investigate this strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel application-to-CGRA allocation approach. By introducing important extensions to the reconfiguration logic and the datapath, we enable the dynamic movement of configurations throughout the fabric and allow overutilized Functional Units (FUs) to recover from stress-induced NBTI aging. Implementing the approach in a resource-constrained state-of-the-art CGRA reveals $2.2\times$ lifetime improvement with negligible performance overheads and less than $10\%$ increase in area.
Human and Machine Action Prediction Independent of Object Information
Venue: arXiv cs.AI 2020
Year: 2020

Predicting other people's action is key to successful social interactions, enabling us to adjust our own behavior to the consequence of the others' future actions. Studies on action recognition have focused on the importance of individual visual features of objects involved in an action and its context. Humans, however, recognize actions on unknown objects or even when objects are imagined (pantomime). Other cues must thus compensate the lack of recognizable visual object features. Here, we focus on the role of inter-object relations that change during an action. We designed a virtual reality setup and tested recognition speed for 10 different manipulation actions on 50 subjects. All objects were abstracted by emulated cubes so the actions could not be inferred using object information. Instead, subjects had to rely only on the information that comes from the changes in the spatial relations that occur between those cubes. In spite of these constraints, our results show the subjects were able to predict actions in, on average, less than 64% of the action's duration. We employed a computational model -an enriched Semantic Event Chain (eSEC)- incorporating the information of spatial relations, specifically (a) objects' touching/untouching, (b) static spatial relations between objects and (c) dynamic spatial relations between objects. Trained on the same actions as those observed by subjects, the model successfully predicted actions even better than humans. Information theoretical analysis shows that eSECs optimally use individual cues, whereas humans presumably mostly rely on a mixed-cue strategy, which takes longer until recognition. Providing a better cognitive basis of action recognition may, on one hand improve our understanding of related human pathologies and, on the other hand, also help to build robots for conflict-free human-robot cooperation. Our results open new avenues here.
ROOT I/O compression improvements for HEP analysis
Venue: arXiv cs.OH 2020
Year: 2020

We overview recent changes in the ROOT I/O system, increasing performance and enhancing it and improving its interaction with other data analysis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment's software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically increased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are significant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space.
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs
Venue: arXiv cs.LG 2020
Year: 2020

Forecasts of product demand are essential for short- and long-term optimization of logistics and production. Thus, the most accurate prediction possible is desirable. In order to optimally train predictive models, the deviation of the forecast compared to the actual demand needs to be assessed by a proper metric. However, if a metric does not represent the actual prediction error, predictive models are insufficiently optimized and, consequently, will yield inaccurate predictions. The most common metrics such as MAPE or RMSE, however, are not suitable for the evaluation of forecasting errors, especially for lumpy and intermittent demand patterns, as they do not sufficiently account for, e.g., temporal shifts (prediction before or after actual demand) or cost-related aspects. Therefore, we propose a novel metric that, in addition to statistical considerations, also addresses business aspects. Additionally, we evaluate the metric based on simulated and real demand time series from the automotive aftermarket.
Real-time Simultaneous 3D Head Modeling and Facial Motion Capture with an RGB-D camera
Venue: arXiv cs.CV 2020
Year: 2020

We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera. Our proposed method is the first one to provide simultaneously comprehensive facial motion tracking and a detailed 3D model of the user's head. Anyone's head can be instantly reconstructed and his facial motion captured without requiring any training or pre-scanning. The user starts facing the camera with a neutral expression in the first frame, but is free to move, talk and change his face expression as he wills otherwise. The facial motion is captured using a blendshape animation model while geometric details are captured using a Deviation image mapped over the template mesh. We contribute with an efficient algorithm to grow and refine the deforming 3D model of the head on-the-fly and in real-time. We demonstrate robust and high-fidelity simultaneous facial motion capture and 3D head modeling results on a wide range of subjects with various head poses and facial expressions.
When and Why is Unsupervised Neural Machine Translation Useless?
Venue: arXiv cs.CL 2020
Year: 2020

This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, we analyze the conditions under which the unsupervised methods fail to produce reasonable translations. We show that their performance is severely affected by linguistic dissimilarity and domain mismatch between source and target monolingual data. Such conditions are common for low-resource language pairs, where unsupervised learning works poorly. In all of our experiments, supervised and semi-supervised baselines with 50k-sentence bilingual data outperform the best unsupervised results. Our analyses pinpoint the limits of the current unsupervised NMT and also suggest immediate research directions.
Derivation of Heard-Of Predicates From Elementary Behavioral Patterns
Venue: arXiv cs.DC 2020
Year: 2020

There are many models of distributed computing, and no unifying mathematical framework for considering them all. One way to sidestep this issue is to start with simple communication and fault models, and use them as building blocks to derive the complex models studied in the field. We thus define operations like union, succession or repetition, which makes it easier to build complex models from simple ones while retaining expressivity. To formalize this approach, we abstract away the complex models and operations in the Heard-Of model. This model relies on (possibly asynchronous) rounds; sequence of digraphs, one for each round, capture which messages sent at a given round are received before the receiver goes to the next round. A set of sequences, called a heard-of predicate,defines the legal communication behaviors -- that is to say, a model of communication. Because the proposed operations behave well with this transformation of operational models into heard-of predicates, we can derive bounds, characterizations, and implementations of the heard-of predicates for the constructions.
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Venue: arXiv cs.LG 2020
Year: 2020

Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software libraries come pre-loaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful, with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e. when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of optimizer proves unexpectedly critical. This reveals many neural network architectures that produce considerable improvements in stability including some with reduced error, for an especially challenging training dataset.
Advanced Persistent Threat: Detection and Defence
Venue: arXiv cs.CR 2020
Year: 2020

The critical assessment presented within this paper explores existing research pertaining to the Advanced Persistent Threat (APT) branch of cyber security, applying the knowledge extracted from this research to discuss, evaluate and opinionate upon the areas of discussion as well as involving personal experiences and knowledge within this field. The synthesis of current literature delves into detection capabilities and techniques as well as defensive solutions for organisations with respect to APTs. Higher-tier detection and defensive strategies bear greater importance with larger organisations; especially government departments or organisations whose work impacts the public on a large scale. Successful APT attacks can result in the exfiltration of sensitive data, network down time and the infection of machines which allow for remote access from Command-and-control (C2) servers. This paper presents a well-rounded analysis of the Advanced Persistent Threat problem and provides well-reasoned conclusions of how to mitigate the security risk.
DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Convolution operator is the core of convolutional neural networks (CNNs) and occupies the most computation cost. To make CNNs more efficient, many methods have been proposed to either design lightweight networks or compress models. Although some efficient network structures have been proposed, such as MobileNet or ShuffleNet, we find that there still exists redundant information between convolution kernels. To address this issue, we propose a novel dynamic convolution method to adaptively generate convolution kernels based on image contents. To demonstrate the effectiveness, we apply dynamic convolution on multiple state-of-the-art CNNs. On one hand, we can reduce the computation cost remarkably while maintaining the performance. For ShuffleNetV2/MobileNetV2/ResNet18/ResNet50, DyNet can reduce 37.0/54.7/67.2/71.3% FLOPs without loss of accuracy. On the other hand, the performance can be largely boosted if the computation cost is maintained. Based on the architecture MobileNetV3-Small/Large, DyNet achieves 70.3/77.1% Top-1 accuracy on ImageNet with an improvement of 2.9/1.9%. To verify the scalability, we also apply DyNet on segmentation task, the results show that DyNet can reduce 69.3% FLOPs while maintaining Mean IoU on segmentation task.
Chip Placement with Deep Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. In particular, as we train over a greater number of chip blocks, our method becomes better at rapidly generating optimized placements for previously unseen chip blocks. To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas. To enable our RL policy to generalize to unseen blocks, we ground representation learning in the supervised task of predicting placement quality. By designing a neural architecture that can accurately predict reward across a wide variety of netlists and their placements, we are able to generate rich feature embeddings of the input netlists. We then use this architecture as the encoder of our policy and value networks to enable transfer learning. Our objective is to minimize PPA (power, performance, and area), and we show that, in under 6 hours, our method can generate placements that are superhuman or comparable on modern accelerator netlists, whereas existing baselines require human experts in the loop and take several weeks.
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Resolution of the complex problem of image retrieval for diagram images has yet to be reached. Deep learning methods continue to excel in the fields of object detection and image classification applied to natural imagery. However, the application of such methodologies applied to binary imagery remains limited due to lack of crucial features such as textures,color and intensity information. This paper presents a deep learning based method for image-based search for binary patent images by taking advantage of existing large natural image repositories for image search and sketch-based methods (Sketches are not identical to diagrams, but they do share some characteristics; for example, both imagery types are gray scale (binary), composed of contours, and are lacking in texture). We begin by using deep learning to generate sketches from natural images for image retrieval and then train a second deep learning model on the sketches. We then use our small set of manually labeled patent diagram images via transfer learning to adapt the image search from sketches of natural images to diagrams. Our experiment results show the effectiveness of deep learning with transfer learning for detecting near-identical copies in patent images and querying similar images based on content.
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking
Venue: arXiv cs.LG 2020
Year: 2020

Some challenging problems in tracking multiple objects include the time-dependent cardinality, unordered measurements and object parameter labeling. In this paper, we employ Bayesian Bayesian nonparametric methods to address these challenges. In particular, we propose modeling the multiple object parameter state prior using the dependent Dirichlet and Pitman-Yor processes. These nonparametric models have been shown to be more flexible and robust, when compared to existing methods, for estimating the time-varying number of objects, providing object labeling and identifying measurement to object associations. Monte Carlo sampling methods are then proposed to efficiently learn the trajectory of objects from noisy measurements. Using simulations, we demonstrate the estimation performance advantage of the new methods when compared to existing algorithms such as the generalized labeled multi-Bernoulli filter.
ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts
Venue: arXiv cs.CL 2020
Year: 2020

In recent years, social media data has exponentially increased, which can be enumerated as one of the largest data repositories in the world. A large portion of this social media data is natural language text. However, the natural language is highly ambiguous due to exposure to the frequent occurrences of entities, which have polysemous words or phrases. Entity linking is the task of linking the entity mentions in the text to their corresponding entities in a knowledge base. Recently, FarsBase, a Persian knowledge graph, has been introduced containing almost half a million entities. In this paper, we propose an unsupervised Persian Entity Linking system, the first entity linking system specially focused on the Persian language, which utilizes context-dependent and context-independent features. For this purpose, we also publish the first entity linking corpus of the Persian language containing 67,595 words that have been crawled from social media texts of some popular channels in the Telegram messenger. The output of the proposed method is 86.94% f-score for the Persian language, which is comparable with the similar state-of-the-art methods in the English language.
A generic cloud migration process model
Venue: arXiv cs.DC 2020
Year: 2020

The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint, focus, and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develop a generic process model and then evaluate and refine the model through three case studies and domain expert reviews. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration models that can be applied to given cloud adoption scenarios.
Some results on Vertex Separator Reconfiguration
Venue: arXiv cs.CC 2020
Year: 2020

We present the first results on the complexity of the reconfiguration of vertex separators under the three most popular rules: token addition/removal, token jumping, and token sliding. We show that, aside from some trivially negative instances, the first two rules are equivalent to each other and that, even if only on a subclass of bipartite graphs, TJ is not equivalent to the other two unless $\mathsf{NP} = \mathsf{PSPACE}$; we do this by showing a relationship between separators and independent sets in this subclass of bipartite graphs. In terms of polynomial time algorithms, we show that every class with a polynomially bounded number of minimal vertex separators admits an efficient algorithm under token jumping, then turn our attention to two classes that do not meet this condition: $\{3P_1, diamond\}$-free and series-parallel graphs. For the first, we describe a novel characterization, which we use to show that reconfiguring vertex separators under token jumping is always possible and that, under token sliding, it can be done in polynomial time; for series-parallel graphs, we also prove that reconfiguration is always possible under TJ and exhibit a polynomial time algorithm to construct the reconfiguration sequence.